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Artif Intell Cancer. Oct 28, 2021; 2(5): 60-68
Published online Oct 28, 2021. doi: 10.35713/aic.v2.i5.60
Repairing the human with artificial intelligence in oncology
Ian Morilla
Ian Morilla, Laboratoire Analyse, Géométrie et Applications - Institut Galilée, Sorbonne Paris Nord University, Paris 75006, France
Author contributions: Morilla I designed and wrote the full manuscript.
Conflict-of-interest statement: Dr. Morilla has nothing to disclose.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ian Morilla, PhD, Assistant Professor, Senior Research Fellow, Laboratoire Analyse, Géométrie et Applications - Institut Galilée, Sorbonne Paris Nord University, 13 Sorbonne-Paris-Cité, Villetaneuse, Paris 75006, France. morilla@math.univ-paris13.fr
Received: October 15, 2021
Peer-review started: October 15, 2021
First decision: October 24, 2021
Revised: October 26, 2021
Accepted: October 27, 2021
Article in press: October 27, 2021
Published online: October 28, 2021
Processing time: 12 Days and 18.2 Hours
Core Tip

Core Tip: In this review, we explore powerful artificial intelligence based models enabling the comprehensive analysis of related problems on oncology. To this end, we described an asserted set of machine learning architectures that goes from the most classical multiple perceptron or neural networks to the novel federated and reinforcement learning designs. Overall, we point out the outgrowth of this mathematical discipline in cancer research and how computational biology and topological features can boost the general performances of these learning models.